Ease the Data Crunch

Above: A vast amount of spatial information can be reviewed and analyzed by integrating data from multiple sources. Right: Consistent standards should be created for the collection of spatial data, including property survey information. Photos: R.A. Smith & Associates Inc.

Gathering data with geoprocessing technologies such as global positioning systems, virtual reference stations, geographic information systems (GIS), and CAD is challenging enough. Integrating the vast amount of spatial information derived from two or more of these technologies to make critical decisions can be overwhelming. The reason for this lies not in technological inadequacy, but rather in the inconsistent use of standards and reference sources that apply to the data being integrated.

Standards are available at the state, local, national, and international levels to help ease the integration and dissemination of data across systems. National and international organizations work together to define standards, typically adopted at the state and local levels, governing how data is collected, modeled, maintained, and disseminated. If local standards are not available, public agencies should observe due diligence to ensure that the data is managed in compliance with state and local law.

Users should implement standards for the original data source (as-builts, orthophotography, legal descriptions, and others) as well as the accompanying metadata that provides the design specifications for the development, use, maintenance, and dissemination of that data. Clearly defined standards assure that the final data product, be it spatial or tabular, will be self-sustaining and can be integrated with other datasets.

Standards are particularly important when collecting information from orthophotography or when creating data using other spatial data as a reference point. In these situations, accuracy of the source information is as critical as properly defining the reference scale and projection, as any errors will be propagated to the derived data. Noticing these propagated errors can be difficult in organizations that have little contact with data beyond their walls. However, when integrating data from outside sources, these errors become magnified and analysis becomes stymied.